The accurate prediction for the travel time can improve bus operation efficiency. The improvement of bus service level and enhancement of bus trip can relieve the urban traffic problems. To predict bus travel time between adjacent signalized intersections, BP neural network model was used. Factors which influence bus travel time were considered as the input of the network model, and bus travel time was used as the output. Bus route No. 3 in Nanjing was chosen as a case study. The results verify model’s feasibility and indicates that the presented model has certain practical values.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Prediction of Bus Travel Time between Adjacent Signalized Intersections Based on BP Neural Network


    Contributors:
    Guo, Yuliang (author) / Yang, Zhen (author) / Wang, Hongneng (author) / Yan, Xue (author) / Liu, Ran (author)

    Conference:

    17th COTA International Conference of Transportation Professionals ; 2017 ; Shanghai, China


    Published in:

    CICTP 2017 ; 1941-1948


    Publication date :

    2018-01-18




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Signalized intersections

    Miller, A.J. | Engineering Index Backfile | 1968



    Minimization of road network travel time by prohibiting left turns at signalized intersections

    Tang, Qinrui / Technische Universität Braunschweig | TIBKAT | 2019

    Free access

    Minimization of road network travel time by prohibiting left turns at signalized intersections

    Tang, Qinrui / Technische Universität Braunschweig | TIBKAT | 2019